Multifocus image fusion is a technique that can integrate the focus information of different source images into a single composite image. At present, most fusion methods still suffer from problems such as block artifacts, artificial edges, halo effects, ringing effects, and contrast reduction. To address these problems, a novel multifocus image fusion method based on a convolutional deep belief network is proposed in this paper. The convolutional operator can effectively extract the focus information of source images, and the focused features extracted from source images can effectively distinguish focused windows from defocused windows. After multiple rounds of training, the convolutional deep belief network model can establish an effective mapping between source images and a score map, which is essential to generate an accurate focus map. Then, the focus map is further modified using binary segmentation and small region filtering, and the final decision map for fusion is obtained. Finally, according to the weights provided by the final decision map, a final fusion image will be formed by fusing multiple source images. The experimental results show that the proposed method is superior to other existing fusion methods in terms of subjective visual effects and objective quantitative evaluation. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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基于卷积深度置信网络的多焦点图像融合方法

多焦点图像融合是一种可以将不同源图像的焦点信息集成到单个合成图像中的技术。目前，大多数融合方法仍然存在诸如块状伪影，人造边缘，光晕效应，振铃效应和对比度降低的问题。针对这些问题，本文提出了一种基于卷积深度置信网络的新型多焦点图像融合方法。卷积算子可以有效地提取源图像的​​聚焦信息，并且从源图像提取的聚焦特征可以有效地将聚焦窗口与散焦窗口区分开。经过多轮训练，卷积深度置信网络模型可以在源图像和得分图之间建立有效的映射，这对于生成准确的聚焦图至关重要。然后，利用二值分割和小区域滤波对聚焦图进行进一步修改，得到融合的最终决策图。最后，根据最终决策图提供的权重，通过融合多个源图像来形成最终融合图像。实验结果表明，该方法在主观视觉效果和客观定量评价上均优于现有的其他融合方法。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。实验结果表明，该方法在主观视觉效果和客观定量评价上均优于现有的其他融合方法。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。实验结果表明，该方法在主观视觉效果和客观定量评价上均优于现有的其他融合方法。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。